{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unsupervised-pixel-level-domain-adaptation","title":"Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks","arxiv_id":"1612.05424","date":"2016-12-16","proceeding":"CVPR 2017 7","authors":["Konstantinos Bousmalis","Nathan Silberman","David Dohan","Dumitru Erhan","Dilip Krishnan"],"abstract":"Collecting well-annotated image datasets to train modern machine learning\nalgorithms is prohibitively expensive for many tasks. One appealing alternative\nis rendering synthetic data where ground-truth annotations are generated\nautomatically. Unfortunately, models trained purely on rendered images often\nfail to generalize to real images. To address this shortcoming, prior work\nintroduced unsupervised domain adaptation algorithms that attempt to map\nrepresentations between the two domains or learn to extract features that are\ndomain-invariant. In this work, we present a new approach that learns, in an\nunsupervised manner, a transformation in the pixel space from one domain to the\nother. Our generative adversarial network (GAN)-based method adapts\nsource-domain images to appear as if drawn from the target domain. Our approach\nnot only produces plausible samples, but also outperforms the state-of-the-art\non a number of unsupervised domain adaptation scenarios by large margins.\nFinally, we demonstrate that the adaptation process generalizes to object\nclasses unseen during training.","url_abs":"http://arxiv.org/abs/1612.05424v2","url_pdf":"http://arxiv.org/pdf/1612.05424v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unsupervised-pixel-level-domain-adaptation","repo_url":"https://github.com/Gitikameher/PixelDA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"unsupervised-pixel-level-domain-adaptation","repo_url":"https://github.com/eriklindernoren/Keras-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"unsupervised-pixel-level-domain-adaptation","repo_url":"https://github.com/eriklindernoren/PyTorch-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"unsupervised-pixel-level-domain-adaptation","repo_url":"https://github.com/francescodisalvo05/66DaysOfData","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"unsupervised-pixel-level-domain-adaptation","repo_url":"https://github.com/tensorflow/models/tree/master/research/domain_adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"unsupervised-pixel-level-domain-adaptation","repo_url":"https://github.com/yjhong89/Domain-Adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.05424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.05424"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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